My SpotifyR report

Author

Alexandra Milli

Tame Impala

WHO is Tame Impala?

Tame Impala is the psychadelic music project of Australian multi-instrumentalist Kevin Parker. In the recording studio, Parker writes, records, performs, and produces all of the project’s music.

As a touring act, Tame Impala consists of Parker (vocals, guitar, synthesizer), Dominic Simper (guitar, synthesiser), Jay Watson (synthesiser, vocals, guitar), Cam Avery (bass guitar, vocals, synthesizer), and Julien Barbagallo (drums, vocals).

Discography

InnerSpeaker (2010)
Lonerism (2012)
Currents (2015)
The Slow Rush (2020)

My favorite album

My favorite album is “Current”. I really like the cover as well.

My favorite song

My favorite song is called “Let It Happen” and it´s also from my favorite album! Let´s listen to it (it´s quite long- so maybe get some snacks)!

My favorite lyrics

She said, “It’s not now or never
Wait ten years, we’ll be together”
I said, “Better late than never
Just don’t make me wait forever”

- The Less I Know the Better, Album: Currents

Data analysis

Data source

I extracted the data using the spotifyr package, and stored it in the data folder.

Import data

# Set the working directory
#setwd("~/Desktop")

library(dplyr)
library(ggplot2)
library(knitr)

# Import data into df
#setwd("~/Desktop")
#getwd()
df <- read.csv("11Tame Impala.csv")

Take a glance of the data frame

# Slice 5 random records in the dataframe
sampled_df <- dplyr::slice_sample(df, n = 5)|> knitr::kable(digits = 2)

# Format the sampled data frame as a table with kableExtra
#library(kableExtra)
#table_result <- kable(sampled_df, format = "html", digits = 2) %>% kable_styling()

# Print the formatted table
#cat(table_result)

# Slice 5 random records in the dataframe
df |> dplyr::slice_sample(n=5) |> knitr::kable(digits = 2)

artist_name	track_name	track_number	album_name	album_release_year	album_release_date	valence	energy	danceability	loudness	tempo	speechiness	instrumentalness	liveness	key_name	mode_name	key_mode	duration_ms
Tame Impala	Apocalypse Dreams	3	Lonerism	2012	2012-01-01	0.27	0.93	0.50	-2.28	128.00	0.05	0.13	0.07	G#	major	G# major	356946
Tame Impala	Borderline	3	The Slow Rush	2020	2020-02-14	0.87	0.87	0.62	-3.07	97.96	0.04	0.00	0.08	F	minor	F minor	237800
Tame Impala	Be Above It	1	Lonerism	2012	2012-01-01	0.38	0.98	0.75	-6.07	118.97	0.07	0.27	0.11	E	minor	E minor	201960
Tame Impala	Mind Mischief	4	Lonerism	2012	2012-01-01	0.82	0.94	0.25	-3.23	161.33	0.05	0.00	0.37	F#	major	F# major	271880
Tame Impala	Expectation	8	InnerSpeaker	2010	2010-05-21	0.18	0.84	0.45	-5.14	139.94	0.06	0.00	0.07	F	major	F major	362466

Data Preparation

Since there are some duplicated tracks, I’ve cleaned them. Also, I removed the album with the live versions.

# Remove records with the same track names
df <- df |> distinct(track_name, .keep_all = TRUE)
# Filter out albums that include "Live Versions"
df <- df |> filter(!grepl("Live Versions", album_name))
# Change the data type of album_name from numeric into character
df$album_name <- as.character(df$album_name)

Album analysis (valence and energy)

# Change album_name from character to factor before plotting
df$album_name <- as.factor(df$album_name)
# Plot a boxplot
ggplot(df, aes(x=album_name, y=valence))+
  geom_boxplot()+
  labs(x="Album name", y="Valence")

Interestingggg, seems like Tame Impala has been the happiest in his last album from 2020! How about his energy?

Energy

ggplot(df, aes(x=album_name, y=energy))+
  geom_boxplot()+
  labs(x="Album name", y="Energy")

Looks like “Lonerism” is the most energetic album!

Fun fact: The songs for “Lonerism” were made world-wide: he has recorded guitar in Vienna and vocals on a Singapore-London flight. He has also lost an iPod somewhere between London and Amsterdam (full with recorded ideas), but he finally finished the album in a rented apartment in Paris in 2011. Such an international album, wow!

I wonder which song from this album is the most energetic one:

library(dplyr)

df %>%
  slice_max(energy, n = 1) %>%
  select(track_name, album_name, album_release_year, energy) %>%
  knitr::kable(digits = 2)

track_name	album_name	album_release_year	energy
Be Above It	Lonerism	2012	0.98

“Be Above It” it is! That´s a good one.

Danceability

Personally, I love dancing in my room to my favorite albums- how about you? Let´s check which album would be the best choice in this case:

ggplot(df, aes(x=album_name, y=danceability))+
  geom_boxplot()+
  labs(x="Album name", y="Danceability")

Seems like it´s “The Slow Rush”!

Speaking of “Rush”, let´s rush into finding Tame Impala´s most positive songs!!!

Most positive songs

df |> slice_max(valence, n=3) |> dplyr::select(track_name, album_name, album_release_year, valence) |> knitr::kable(digits = 2)

track_name	album_name	album_release_year	valence
Borderline	The Slow Rush	2020	0.87
Mind Mischief	Lonerism	2012	0.82
Disciples	Currents	2015	0.80

Fun fact: Borderline is one of my favorite songs! Let´s give it a listen for some positive feelings!

General Mood

Let´s take a look at the valence and energy levels together to get an overview of the albums´ general mood:

library(ggplot2)
library(ggplot2)

ggplot(df, aes(x = valence, y = energy, color = album_name)) +
  geom_point() +
  geom_hline(yintercept = 0.5, color = "grey") +
  geom_vline(xintercept = 0.5, color = "grey")

Impressive! The albums appear to be energetically charged! In terms of valence, “Lonerism,” true to its name, leans towards a more negative and melancholic tone, whereas the other albums encompass a spectrum of emotions, from sadness to joy!

Conclusion

So, in the cosmic dance of Tame Impala’s SpotifyR report, it’s not just data; it’s a funky odyssey through beats, rhythms, and vibes. From head-nodding beats to soul-searching melodies, Tame Impala’s musical universe is a groove-packed journey.

Thank you for reading!